Kenneth C. Arnold kcarnold

Example based on https://github.com/UKPLab/sentence-transformers/blob/master/examples/applications/clustering/fast_clustering.py

# Install the needed libraries
!pip install -q sentence-transformers

# Import libraries we'll need
from sentence_transformers import SentenceTransformer, util
import os
import csv

	import sqlite3
	from pathlib import Path
	from collections import defaultdict
	import json
	from lxml import etree


	def decode_richtextXML(xml):
	# _richtextfield:Lyrics <Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="I love You Lord" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="Oh Your mercy never fails me" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="All my days" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="I’ve been held in Your hands" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0" /><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="From the moment that I wake up" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="Until I lay my head" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="I will sing of the goodness of God" /></Paragraph><Paragraph Language="en-US" Margin="0,0,0,0" /><Paragraph Language="en-US" Margin="0,0,0,0"><Run Text="All my life You have been faithful"

	import sqlite3
	from pathlib import Path
	from collections import defaultdict
	import json

	# NOTE: This will need to be changed to your data path
	proclaim_data = Path('~/Library/Application Support/Proclaim/Data/5sos6hqf.xyd/').expanduser()
	presentations_db = proclaim_data / 'PresentationManager' / 'PresentationManager.db'

	conn = sqlite3.connect(presentations_db)

	// On a when2meet poll that has already been filled out:
	//
	// 1. Right-click the timeslot corresponding to the first time you chose and click Inspect. (If you don't see that, turn on your browser devtools.)
	// 2. Find the attribute that looks like data-time="279900000". Use that number for the "slot id" when prompted.
	// 3. Copy and paste this script into the browser Console.
	//
	// You'll now see the availability of everyone who was not available at the first time you chose.

	// Find everyone available then
	slotId = +prompt("Slot id?"); // find this by right-click on the slot element in the right-side view

	import streamlit as st
	import random

	words = ['hello', 'world', 'goodbye', 'moon']
	random.shuffle(words)

	essay = st.text_area("Essay", "", key="essay")

	def append_word(word):
	st.session_state['essay'] = (

	import streamlit as st
	import os
	from openai import OpenAI

	# load the API key from a .env file
	from dotenv import load_dotenv
	load_dotenv()


	st.title("Recipe Finder")

	// Run this on the Core Curriculum page
	// Gather courses by section
	courses = new Map();
	document.querySelectorAll('.acalog-course').forEach(x => {
	let name = x.textContent;
	let section = x.closest('.acalog-core').querySelector('h3, h4').textContent;
	console.log(name, section);
	if (!courses.has(name)) {courses.set(name, [])}
	courses.get(name).push(section);
	});

	window.FixPlotlySize = {
	id: 'FixPlotlySize',
	init: function(Reveal) {
	Reveal.addEventListener( 'slidechanged', function( event ) {
	let curSlide = Reveal.getCurrentSlide();
	require(['plotly'], function(Plotly) {
	let plotlyDivs = curSlide.querySelectorAll('.js-plotly-plot');
	for (let i = 0; i < plotlyDivs.length; i++) {
	Plotly.Plots.resize(plotlyDivs[i]);
	}

	name: Comment Insertion updated version
	description: >-
	Update: inserting comments on words that are loaded from a infoList from the
	(fake) server
	host: WORD
	api_set: {}
	script:
	content: \|
	// Given the target word and the comment, insert an anchored comment.
	function addComment(paragraphIndex: number, targetWord: any) {